Forecasting banking system liquidity is crucial for the effective monetary policy implementation. This study investigates the effectiveness of various econometric and machine learning models in predicting the autonomous factors of banking system liquidity. The research compares widely used...

Forecasting banking system liquidity is crucial for the effective monetary policy implementation. This study investigates the effectiveness of various econometric and machine learning models in predicting the autonomous factors of banking system liquidity. The research compares widely used...

The notion that investors shift to gold during economic market crises remains unverifed for many cryptocurrency markets. This paper investigates the connectedness between the 10 most traded cryptocurrencies and gold as well as crude oil markets pre-COVID-19 and during COVID-19. Through the...

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting...

The firm size and value anomalies are the global-level counterpart for explaining the cross-sectional variations of equity returns. The purpose of this paper is to examine the size, value effects and the explanatory power of three well-known pricing models - CAPM, three-and five-factor across...